The use of ultrasound to produce images for medical monitoring and diagnosis has become wide spread to a large extent as a result from its nonionizing nature and its ability to produce images resulting from the inherent differences in properties of various soft tissues. Typical and common applications include examination and monitoring of the heart, abdomen and fetus. In most areas, diagnosis is now generally based on the size, position, contour and motion of the studied structures as well as on their relative transmission and reflection properties.
In general, for a typical ultrasound scanner, a user needs to perform multiple operations to obtain optimized images, which is time consuming and operator dependent. Furthermore, an inexperienced user may generate sub-optimal images due to sub-optimal settings that may increase the risk of an incorrect diagnosis.
In order to cope with these problems, a common procedure and practice is to use pre-set system parameters for the imaging procedure for each clinical application. In this case, the scanner may provide a good performance on an average patient without any user input. However, this approach does not take into account any patient specific variations, which in fact is very important in ultrasound imaging to obtain an optimal image quality.
In the prior art, there have been made a large number of attempts to provide automatic image optimization. For example, in U.S. Pat. No. 8,235,905 to Feng et al. discloses methods and systems for automatic image optimization for ultrasound imaging including evaluation of an image quality cost function in order to produce an image quality metric. The image quality metric is used to compare different images with each other to determine whether a maximized image quality metric has been reached. The signal parameters that produced the maximized image quality metric are assigned as optimum parameters.
U.S. Pat. No. 5,579,768 to Klesensi discloses methods and systems for automatic gain compensation in an ultrasound imaging system. Imaging data is partitioned into small zones (e.g. such as regions selected in response to range and in response to azimuth, or both). At least one zone imaging value is determined for each zone corresponding to at least one measure of an imaging value for the signals reflected from objects or structures being imaged in that zone. The measures can be mean value or standard deviation. The imaging value can be an intensity value. Each zone is examined to determine whether its imaging values are within selected numeric range. An attenuation curve is formed in response to the selected zone intensity values. A gain compensation curve is determined from the attenuation curve.
U.S. Pat. No. 5,993,392 to Roundhill et al. discloses methods and systems for variation of dynamic range of ultrasonic image data as function of scanning depth and lateral dimension. The displayed dynamic range and noise rejection level are varied with both range (depth) and lateral (scanline to scanline) dimensions of an ultrasonic image.
U.S. Pat. No. 6,743,174 to Ng et al. is directed to automatic compensation for variations in brightness or contrast in ultrasonic diagnostic images. This is performed by computing offsets to a nominal TGC (“Time Gain Compensation”) curve which will compensate for depth dependent attenuation. The offsets to the TGC curve are then applied to subsequent images. Image dynamic range may alternatively be used for the optimization. In particular, line fit is used and scanlines exhibiting poor coupling or low signal levels are discarded and the line fit is executed on the retained data. A linear slope is fitted to the depth-dependent attenuation characteristics of each scanline in the image and these slopes are combined in to a single slope value for the image.
U.S. Pat. No. 8,357,094 discloses methods and systems for adaptive system parameter optimization of ultrasound imaging systems. Fuzzy logic is used to adaptively adjust system parameters for imaging modes. A neural network may perform certain functions separately or in conjunction with the fuzzy logic. The neural network is configured to adapt functions of ultrasound image generating systems based on patient type, user preference and system operating conditions. The neural network is used in applications including detection of anatomical features, e.g. a main vessel, disease classification, and selection of features from different image modalities to obtain a composite image.
U.S. Pat. No. 8,090,165 to Jiang et al. is directed to gray-scale optimization of ultrasonic images. A searching device is arranged to search non-evenly divided sub-areas in an ultrasonic image. An analyzing device is arranged to analyze a change of gray level in each sub-area in the direction of depth. Based on the analysis, an optimized gray level value is calculated. In particular, the noise level is analyzed, a grey level changing curve (in a direction of depth) is analyzed, and a PDM is obtained for the image, where PDM is an abbreviation for “Parameter for Digital Time Gain Compensation Module”.
However, despite these numerous prior art methods and systems there is still a need for improved methods and systems for automatic control and optimization of system parameters of ultrasound imaging systems.